Blogs

An AI-native SDLC is a software development lifecycle where AI's autonomy is explicitly defined at each stage and the workflow is rebuilt around that definition.

When a CTO says "we need to replace our SaaS stack," the conversation usually starts in the wrong place: which vendor, what price, which integrations. That framing assumes the thing being replaced is itself a SaaS product with a comparable substitute sitting in a G2 category page.

Most enterprise engineering organizations are not deciding whether to adopt AI coding agents anymore. That decision is already behind them. What they are actually deciding, often without realizing it, is whether the velocity those agents produced in the first two quarters holds up once the codebase, the team count, and the audit scrutiny all grow at the same time. This piece walks through what has to be true for that velocity to hold, section by section, starting with where specification actually belongs in your delivery lifecycle.

License an AI coding assistant, roll it out to the team, wait for velocity to show up in the numbers. The first draft does come faster. That part is real, and it's also the entire extent of what changes on its own.

Your engineers are already using Cursor, Copilot, Claude Code, and other AI tools. But if requirements, architecture, development, testing, review, and release still operate the same way, you haven't built an AI-native SDLC. You've built an AI-augmented one.